Jonathan Rubin

2023

Jonathan Rubin, Jason Crowley, George Leung, Morteza Ziyadi, Maria Minakova.
Entity Contrastive Learning in a Large-Scale Virtual Assistant System
Association for Computational Linguistics, Industry Track, ACL 2023.

Li Chen, Jonathan Rubin, Jiahong Ouyang, Naveen Balaraju, Shubham Patil, Courosh Mehanian, Sourabh Kulhare, Rachel Millin, Kenton W. Gregory, Cynthia R. Gregory, Meihua Zhu, David O. Kessler, Laurie Malia, Almaz Dessie, Joni Rabiner, Di Coneybeare, Bo Shopsin, Andrew Hersh, Cristian Madar, Jeffrey Shupp, Laura S. Johnson, Jacob Avila, Kristin Dwyer, Peter Weimersheimer, Balasundar Raju, Jochen Kruecker, Alvin Chen.
Self-Supervised Learning with Spatio-Temporal Augmentation for Lung Ultrasound Video Analysis
IEEE International Symposium on Biomedical Imaging, ISBI 2023.

2021

Jonathan Rubin, Ramon Erkamp, Ragha Srinivasa Naidu, Anumod Odungatta Thodiyil, Alvin Chen.
Attention Distillation for Detection Transformers: Application to Real-Time Video Object Detection in Ultrasound Machine Learning for Health, ML4H 2021.

Annamalai Natarajan, Gregory Boverman, Yale Chang, Corneliu Antonescu and Jonathan Rubin.
Convolution-Free Waveform Transformers for Multi-Lead ECG Classification
Computing in Cardiology 2021.

Asif Rahman, Yale Chang, Jonathan Rubin.
Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series
arXiv preprint arXiv:2109.07602 (2021).

Jonathan Rubin, Alvin Chen, Anumod Odungattu Thodiyil, Raghavendra Srinivasa Naidu, Ramon Erkamp, Jon Fincke, Balasundar Raju.
Efficient Video-Based Deep Learning for Ultrasound Guided Needle Insertion
Medical Imaging with Deep Learning, MIDL 2021.

2020

Annamalai Natarajan, Yale Chang, Sara Mariani, Asif Rahman, Gregory Boverman, Shruti Vij and Jonathan Rubin.
A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification
Computing in Cardiology 2020.
*1st place PhysioNet Challenge 2020

Yumin Liu, Claire Zhao, Jonathan Rubin.
Uncertainty Quantification in Chest X-Ray Image Classification using Bayesian Deep Neural Networks
European Conference on Artificial Intelligence, Knowledge Discovery in Healthcare Data Workshop, ECAI 2020.

2019

Yale Chang, Jonathan Rubin, Gregory Boverman, Shruti Vij, Asif Rahman, Annamalai Natarajan, Saman Parvaneh.
A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series
Computing in Cardiology 2019.
*2nd place PhysioNet Challenge Hackathon 2019

Xin Wang, Evan Schwab, Jonathan Rubin, Prescott Klassen, Ruizhi Liao, Seth Berkowitz, Polina Golland, Steven Horng and Sandeep Dalal.
Pulmonary Edema Severity Estimation in Chest Radiographs Using Deep Learning
International Conference on Medical Imaging with Deep Learning, MIDL 2019.

Jonathan Rubin and S. Mazdak Abulnaga.
CT-To-MR Conditional Generative Adversarial Networks for Improved Stroke Lesion Segmentation
Seventh IEEE International Conference on Healthcare Informatics, ICHI 2019.

Ruizhi Liao, Jonathan Rubin, Grace Lam, Seth Berkowitz, Sandeep Dalal, William Wells, Steven Horng, and Polina Golland. Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
arXiv preprint arXiv:1902.10785 (2019).

2018

Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin and Una-May O'Reilly.
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection
Neural Information Processing Systems, Machine Learning for Health (ML4H) Workshop, NeurIPS 2018.

S. Mazdak Abulnaga & Jonathan Rubin.
Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss
MICCAI 2018, BrainLes workshop.

Jonathan Rubin, Deepan Sanghavi, Claire Zhao, Kathy Lee, Ashequl Qadir, Minnan Xu-Wilson.
Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks
arXiv preprint arXiv:1804.07839v2 (2018).

Jonathan Rubin, Saman Parvaneh, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018).
Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings
International Society of Electrocardiology Conference (2018).
*Invited Talk

Jonathan Rubin, Cristhian Potes, Minnan Xu-Wilson, Junzi Dong, Asif Rahman, Hiep Nguyen, and David Moromisato.
An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit
International Journal of Medical Informatics (2018).

2017

Jonathan Rubin, Saman Parvaneh, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2017).
Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings
Computing in Cardiology 2017.

Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei and Kumar Sricharan (2017).
Recognizing Abnormal Heart Sounds Using Deep Learning
International Joint Conference on Artificial Intelligence, Knowledge Discovery in Healthcare Workshop , IJCAI 2017.

2016

Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei and Kumar Sricharan (2016).
Classifying Heart Sound Recordings using Deep Convolutional Neural Networks and Mel-Frequency Cepstral Coefficients, Computing in Cardiology 2016.

Jonathan Rubin, Rui Abreu, Shane Ahern, Hoda Eldardiry and Daniel G. Bobrow (2016).
Time, Frequency & Complexity Analysis for Recognizing Panic States from Physiologic Time-Series
International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2016.

2015

Luis Cruz, Jonathan Rubin, Rui Abreu, Shane Ahern, Hoda Eldardiry and Daniel G. Bobrow (2015).
A Wearable and Mobile Intervention Delivery System for Individuals with Panic Disorder
International Conference on Mobile and Ubiquitous Multimedia, MUM 2015.

Jonathan Rubin, Hoda Eldardiry, Rui Abreu, Shane Ahern, Honglu Du, Ashish Pattekar and Daniel G. Bobrow
Towards a Mobile and Wearable System for Predicting Panic Attacks
ACM Conference on Ubiquitous Computing, Ubicomp ’15.

2013

Jonathan Rubin & Ian Watson.
Decision Generalisation from Game Logs in No Limit Texas Hold'em
International Joint Conference on Artificial Intelligence, IJCAI 2013.

Nolan Bard, John Alexander Hawkin, Jonathan Rubin and Martin Zinkevich.
The Annual Computer Poker Competition
AI Magazine, 34(2):112–, 2013

Michael Silva, Silas McCroskey, Jonathan Rubin, Michael Youngblood and Ashwin Ram.
Learning from Demonstration to Be a Good Team Member in a Role Playing Game
International Florida Artificial Intelligence Research Society Conference, FLAIRS 2013.

2012

Jonathan Rubin & Ian Watson
Case-Based Strategies in Computer Poker
AI Communications, Volume 25, Number 1: 19-48, March 2012.

2011

Jonathan Rubin & Ian Watson.
Successful Performance via Decision Generalisation in No Limit Texas Hold'em
International Conference on Case-Based Reasoning, ICCBR 2011.
*Best Application Paper

Jonathan Rubin & Ian Watson.
On Combining Decisions from Multiple Expert Imitators for Performance
International Joint Conference on Artificial Intelligence, IJCAI 2011.

Jonathan Rubin & Ian Watson.
Computer poker: A review,
Artificial Intelligence, 175(5-6):958-987, April 2011.

2010

Jonathan Rubin & Ian Watson.
Similarity-Based Retrieval and Solution Re-use Policies in the Game of Texas Hold'em
International Conference on Case-Based Reasoning, ICCBR 2010.

2009

Jonathan Rubin & Ian Watson.
A Memory-Based Approach to Two-Player Texas Hold'em
Advances in Artificial Intelligence, 22nd Australasian Joint Conference 2009

Jonathan Rubin & Ian Watson.
Memory and Analogy in Game-Playing Agents.
International Conference on Case-Based Reasoning, Workshop on Case-Based Reasoning for Computer Games, ICCBR 2009.

2008

Ian Watson & Jonathan Rubin
Casper: a Case-Based Poker-Bot.
Advances in Artificial Intelligence, 21st Australasian Joint Conference on Artificial Intelligence 2008.

2007

Jonathan Rubin & Ian Watson.
Investigating the Effectiveness of Applying Case-Based Reasoning to the game of Texas Hold’em.
Florida Artificial Intelligence Research Society Conference, FLAIRS 2007.

2005

Jonathan Rubin, Burkhard C. Wuensche, Linda Cameron and Carey Stevens
Animation and Modelling of Cardiac Performance for Patient Monitoring
Image and Vision Computing New Zealand, IVCNZ 2005.